Braid Health Granted FDA Clearance for AI-Powered Diagnostic Collaboration Platform
The Braid mobile application lets providers and radiologists access, review, and annotate images and share results with patients in real-time—from any mobile device.
Category: AI & Machine Learning
AI & Machine Learning
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AI & Machine Learning
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Aidoc Gets Series B Extension to $47M in Funding for Radiology AI
Radiology AI company Aidoc has raised an additional $20 million, bringing the total amount of funding raised by the company to $60 million.
AI & Machine Learning
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Why Radiologic Technologists Should Embrace Artificial Intelligence
Artificial intelligence is changing the role of radiologic technologists (RTs) by increasing the efficiency and accuracy with which exams are done. That’s just one reason RT supervisor Russell Anthony, AART, is optimistic about the ability of artificial intelligence to transform his daily work.
Can you sue an algorithm for malpractice? It depends
Artificial intelligence (AI) is starting to gain a foothold in modern medicine, for tasks such as identifying anomalies in CT scans and other diagnostic images. But who is liable for mistakes made based on AI? The hospital? The physicians? The manufacturer of the system?
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AI Improves Radiologists’ Readings of Mammograms
Machine-learning algorithms could help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists, according to a study published in JAMA Network Open.
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FDA Authorizes Marketing of First AI Cardiac Ultrasound Software
The U.S. FDA has authorized the marketing of software to assist medical professionals in the acquisition of cardiac ultrasound, or echocardiography, images.
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AI Helps Find Signs of Heart Disease on Lung Cancer Screens
Artificial intelligence provides an automated and accurate tool to measure a common marker of heart disease in patients getting chest CT scans for lung cancer screening, according to a study presented earlier this month at the annual meeting of the Radiological Society of North America.
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Ensembling Improves Machine Learning Model Performance
Ensembles created using models submitted to the Radiological Society of North America Pediatric Bone Age Machine Learning Challenge convincingly outperformed single-model prediction of bone age, according to a study published in the journal Radiology: Artificial Intelligence.
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